Non disponibile in italiano
Apostolos Apostolou
- 24 January 2023
- WORKING PAPER SERIES - No. 2767Details
- Abstract
- We develop a measure of overall financial risk in China by applying machine learning techniques to textual data. A pre-defined set of relevant newspaper articles is first selected using a specific constellation of risk-related keywords. Then, we employ topical modelling based on an unsupervised machine learning algorithm to decompose financial risk into its thematic drivers. The resulting aggregated indicator can identify major episodes of overall heightened financial risks in China, which cannot be consistently captured using financial data. Finally, a structural VAR framework is employed to show that shocks to the financial risk measure have a significant impact on macroeconomic and financial variables in China and abroad.
- JEL Code
- C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
C65 : Mathematical and Quantitative Methods→Mathematical Methods, Programming Models, Mathematical and Simulation Modeling→Miscellaneous Mathematical Tools
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
F44 : International Economics→Macroeconomic Aspects of International Trade and Finance→International Business Cycles
G15 : Financial Economics→General Financial Markets→International Financial Markets
- 24 March 2022
- ECONOMIC BULLETIN - ARTICLEEconomic Bulletin Issue 2, 2022Details
- Abstract
- Recent tensions in China’s real estate market have highlighted the risks inherent in the country’s highly leveraged corporate sector. These risks have been building up for some time, as high investment rates have coincided with high levels of debt accumulation. Moreover, the source of debt has moved beyond the traditional banking sector, with non-bank financial institutions providing financing which is less stable and more susceptible to sudden changes in investor sentiment. In addition, tensions in large corporate sectors could be transmitted to the rest of the economy through a number of channels. These channels include households, which are themselves increasingly leveraged and whose wealth is significantly exposed to the real estate market. A wider Chinese growth slowdown could, in turn, have global repercussions, given the size of the Chinese economy, its important global trade linkages and the central role it plays in international commodity markets. Against this backdrop, this article will review the rise in financial risks in China’s economy stemming from increasing private sector leverage, the interconnectedness between the financial and non-bank financial sectors, and households’ rising debt exposures.
- JEL Code
- E5 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit
E6 : Macroeconomics and Monetary Economics→Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook
G2 : Financial Economics→Financial Institutions and Services
G5 : Financial Economics
- 21 September 2020
- ECONOMIC BULLETIN - ARTICLEEconomic Bulletin Issue 6, 2020Details
- Abstract
- This article will trace the decline and subsequent recovery of China’s economy following the outbreak of the coronavirus (COVID-19). It employs high-frequency data to assess the speed at which activity in different sectors of the economy is normalising after businesses were allowed to resume operations. One particular focus will be on differentiating between the industrial and services sectors, which are subject to different health and safety measures. The article finds that China’s economic activity rose from a trough of around 20% of normal levels in February 2020 to 90% in the span of just three months. While production capacity recovered swiftly, activity normalised more gradually in the services sector, where COVID-19 containment measures had continued to weigh heavily. The recovery was driven primarily by private domestic demand and the authorities’ policy response, as the normalisation in China coincided with the implementation of lockdown measures by many of its trading partners and hence also with a fall in external demand. Looking ahead, uncertainty and risks surrounding the recovery path remain exceptionally high, owing in large part to the uncertainty regarding how the COVID-19 pandemic will develop and if and when a medical solution to the virus can be found.
- JEL Code
- E2 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy
E5 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit
E6 : Macroeconomics and Monetary Economics→Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook
F1 : International Economics→Trade
G1 : Financial Economics→General Financial Markets
- 12 April 2017
- WORKING PAPER SERIES - No. 2044Details
- Abstract
- This paper examines volatility spillovers from changes in the size of the balance sheets of the Federal Reserve FED) and European Central Bank (ECB) to emerging market economies (EMEs) from 2003 to 2014. We find that EME bond markets are most susceptible to positive volatility spillovers from both the FED and ECB in terms of magnitude. Positive volatility spillovers to EME currency markets are higher in the case of FED balance sheet expansions than those of the ECB by a factor of about ten. By contrast, we find that EME stock markets are subject to negative volatility spillovers. Moreover, we find only limited evidence of volatility transmission to the real economy of EMEs following the monetary policy actions of the FED and ECB. Finally, we show that the proportion of the volatility in EMEs that is accounted for by changes in FED and ECB balance sheets shifts over time.
- JEL Code
- F3 : International Economics→International Finance
F4 : International Economics→Macroeconomic Aspects of International Trade and Finance
F16 : International Economics→Trade→Trade and Labor Market Interactions
G1 : Financial Economics→General Financial Markets